Recursion, Backtracking and Dynamic Programming in Python

Recursion, Backtracking and Dynamic Programming in Python - Learn Competitive Programming, Recursion, Backtracking, Divide and Conquer Methods and Dynamic Programming in Python

Preview this Course

  GET COUPON CODE

This course is about the fundamental concepts of algorithmic problems focusing on recursion, backtracking, dynamic programming and divide and conquer approaches. As far as I am concerned, these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D.

Section 1 - RECURSION

what are recursion and recursive methods

stack memory and heap memory overview

what is stack overflow?

Fibonacci numbers

factorial function

tower of Hanoi problem

Section 2 - SEARCH ALGORITHMS

linear search approach

binary search algorithm

Section 3 - SELECTION ALGORITHMS

what are selection algorithms?

Hoare's algorithm

how to find the k-th order statistics in O(N) linear running time?

quickselect algorithm

median of medians algorithm

the secretary problem

Section 4 - BIT MANIPULATION PROBLEMS

binary numbers

logical operators and shift operators

checking even and odd numbers

bit length problem

Russian peasant multiplication

Section 5 - BACKTRACKING

what is backtracking?

n-queens problem

Hamiltonian cycle problem

coloring problem

knight's tour problem

maze problem

Sudoku problem

Section 6 - DYNAMIC PROGRAMMING

what is dynamic programming?

knapsack problem

rod cutting problem

subset sum problem

Kadane's algorithm

longest common subsequence (LCS) problem

Section 7 - OPTIMAL PACKING 

what is optimal packing?

bin packing problem

Section 8 - DIVIDE AND CONQUER APPROACHES

what is the divide and conquer approach?

dynamic programming and divide and conquer method

how to achieve sorting in O(NlogN) with merge sort?

the closest pair of points problem

Section 9 - Substring Search Algorithms

substring search algorithms

brute-force substring search

Z substring search algorithm

Rabin-Karp algorithm and hashing

Knuth-Morris-Pratt (KMP) substring search algorithm

Section 10 - COMMON INTERVIEW QUESTIONS

top interview questions (Google, Facebook and Amazon)

anagram problem

palindrome problem

integer reversion problem

dutch national flag problem

trapping rain water problem

Section 11 - Algorithms Analysis

how to measure the running time of algorithms

running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations

complexity classes

polynomial (P) and non-deterministic polynomial (NP) algorithms

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together from scratch in Python.

Thanks for joining the course, let's get started!

Who this course is for:

• This course is meant for newbies who are not familiar with algorithmic problems in the main or students looking for some refresher
• Anyone preparing for programming interviews or interested in improving their problem solving skills